Manual Secondary School External Examination Systems: Reliability, Robustness and Resilience

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Systems Engineering , 16 2 , — A metric and frameworks for resilience analysis of engineered and infrastructure systems. A framework for assessing the resilience of infrastructure and economic systems. In Sustainable and resilient critical infrastructure systems pp.

Berlin, Heidelberg: Springer. Disaster resilience: A national imperative. Environment: Science and Policy for Sustainable Development , 55 2 , 25 — Homeland security preparedness: Balancing protection with resilience in emergent systems.

Systems Engineering , 11 4 , — Washington, D. Critical infrastructure resilience: Final report and recommendations : National Infrastructure Advisory Council. Furthermore, most definitions also recognize that resilience is not the sum or average of the resilience of its components, which further complicates its actual measurement and operationalization Hosseini et al.

To capture its full complexity and transform resilience into a measurable concept, many frameworks suggest to describe and measure the classical resilience curve using different functions of time as the basic variable H. The diversity of concepts and definitions of resilience is high. For instance, many national and international institutions proposed their own definitions as a result of the calls for public policy improvements considering critical infrastructure protection CIPedia, CIPedia. We select the resilience functions considered in a recent comprehensive review by Hosseini et al.

Resilience and risk: Methods and application in environment, cyber and social domains. Dordrecht, The Netherlands: Springer.


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Selected resilience functions located along the time roughly and system performance axes to indicate where they come to play in the resilience process. Figure 2. The resilience functions listed in Figure 2 can be classified according to the phase and the gradient of the resilience process. The pre-event phase is widely investigated and well-understood for many systems and resilience research fields using mainly the established risk assessment and management methods. This indicates that there is a need for better and more comprehensive resilience assessment approaches.

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This is a result of giving the same word different meanings. We give in Figure 2 various resilience concepts, including functions, and illustrate that all the given concepts are meaningful and describe in a similar way aspects of a system experiencing a disruptive event throughout time. A comprehensive definition of resilience functions, inspired by biophysical systems and aimed at integrating the multitude of the above-mentioned resilience functions, was proposed recently H. It corresponds, for example, to the elasticity threshold limit in materials science and is very similar to the concept of reliability.

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Robustness and absorptivity are related to this function. It comprises the shape of the recovery curve and recovery speed. A system can be reconfigured to achieve better performances than before the disruption. Those questions, and the corresponding resilience functions are broadly covering all the important features of resilience from a critical energy infrastructure perspective.

Consequently, this review uses these biophysical resilience functions to consistently classify and analyse the compiled set of energy infrastructure resilience studies with respect to how they consider the resilience curve.

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The literature on resilience assessment of energy systems is multidisciplinary and covers a broad range of concepts, approaches, methods and case study applications. As presented in Section 3 , the variety of resilience concepts results in many resilience functions that are partially overlapping or used to designate different aspects of the resilience process. This further complicates a direct comparison between studies analysing the resilience of energy systems. To overcome this obstacle, we adopted the structured paper selection, assessment and classification methodology illustrated in Figure 3.

Flowchart of the literature screening methodology used in this review. Figure 3. First, we analysed five recently published review studies including their references, namely Hosseini et al. We assumed that the publications collected from these studies provide a good starting point for our analysis.

A review on resilience assessment of energy systems

Web of science. Google Scholar. A non-exhaustive list of the keywords used is: resilience, energy, power network, power system, electricity, gas, infrastructure, interdependencies, quantification, risk, complex networks, resist, reliability, robustness, restabilize, rebuild, recovery, and reconfigurability. Afterwards, we conducted a relevance judgement and refinement process to remove studies that were not related to the energy sector.

For this purpose, we used a two-tier approach: 1 solely on the abstract and 2 on the full paper content.

Hence, if after reading the abstract it was still unclear whether the paper was relevant for the present study, we based our final decision on the full paper content. This selection process resulted in a final set of energy-related resilience studies. Subsequently, we further assessed and classified each item.

First, it was assigned to one of two assessment approaches, i. Qualitative approaches assess resilience without using numerical values, formulas or models. Quantitative studies were then further differentiated into semi-quantitative, deterministic and probabilistic stochastic approaches. Quantitative studies rely on numerical data, employ mathematical models to describe relationships, or use indicators measured with interval or ratio scales. Second, we identified the modelling approach used complex networks, agent-based modelling, fuzzy logic, etc.

Third, for each study, we identified the resilience functions according to Section 3. These four functions cover resilience comprehensively, i. Hence, they are useful to establish a harmonized resilience function classification of all studies because not all of them employ the same set of resilience functions. Fourth, we assigned the disruptive events considered in each study to the following broad categories: natural disasters, technical failures, malicious attacks, geopolitical or generic disruptions, etc.

Fifth, we identified the system analysed, such as electric power system fictive or real case study , components of the electric power system, types of power plants, natural gas networks, etc. Sixth, as most of the energy-related resilience studies handle either the electric power system or the oil and gas sector, we categorized each paper into either 1 the electric power sector, 2 the oil and gas sector, or 3 other energy-related sectors.

Seventh, if the topic of sustainability was addressed in the paper, we established the relations with resilience and analysed how both concepts were framed.

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The complete list of energy-related resilience studies that was compiled and subsequently categorized is shown in Table S1. This list was used to produce Figure 4 and 5. Wang et al. Efficient test and visualization of multi-set intersections. Scientific Reports , 5, This package calculates the frequencies of each possible intersection and their statistical significances in terms of p-values.

Compared to the original package, which uses a Markov-Chains Monte Carlo framework for computing the exact statistical distributions of multi-set intersections, the p-values are not displayed. In fact, the present dataset consisting of references is too small for the algorithm to converge. To deal with all the possible intersections in the dataset under interest, we applied the SuperExactTest function, which is able to deal with 2 m -1 intersections for m sets automatically.

We removed the intersections considering simultaneously both qualitative and quantitative assessment approaches from Figure 4. Hence, these intersections are empty. Circular plot showing all possible intersections and the corresponding numbers of studies intersections. The height of the bars in the outer layer refers to the intersection size. Figure 4. Figure 5. Keyword co-occurrence network. The size of the nodes are proportional to the number of occurrences of the keywords.

The colours represent the clusters, which were calculated based on a maximization of the association strengths between nodes. Visualizing bibliometric networks. In Measuring scholarly impact pp.